{"id":"W4390342889","doi":"10.5539/ijsp.v12n6p23","title":"Integer-Valued First Order Autoregressive (INAR(1)) Model With Negative Binomial (NB) Innovation For The Forecasting Of Time Series Count Data","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Negative binomial distribution; Estimator; Autoregressive model; Statistics; Count data; Series (stratigraphy); Mean squared error; Integer (computer science); Poisson distribution; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004129956,0.0006663107,0.0009900215,0.0009045683,0.000415599,0.001240007,0.001958499,0.00120523,0.001662573],"category_scores_gemma":[0.01116637,0.0003798471,0.001117444,0.00123104,0.0007692673,0.001793071,0.0005602759,0.002403409,0.0004510964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009741422,"about_ca_system_score_gemma":0.0008056271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01087287,"about_ca_topic_score_gemma":0.007072061,"domain_scores_codex":[0.9980756,0.0008395493,0.00008418869,0.0004440547,0.0003895575,0.0001669607],"domain_scores_gemma":[0.9956696,0.003112642,0.0005193737,0.0001687305,0.0004580097,0.00007159047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001436986,0.00008670456,0.01296921,0.0003104391,0.0001527751,0.00050309,0.0004650227,0.7587279,0.001767971,0.1761932,0.002735931,0.04594413],"study_design_scores_gemma":[0.000006912279,0.00002774609,0.0009313134,0.00001926929,0.00002504435,0.00005492923,0.0000272115,0.9818792,0.0001835261,0.01586845,0.0009610887,0.00001537367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06920443,0.001742386,0.9230705,0.0009649935,0.0002496541,0.00006847076,0.0003303083,0.0003648837,0.004004384],"genre_scores_gemma":[0.8959938,0.002446921,0.09270833,0.0003204338,0.0002883963,0.0002354845,0.0007109793,0.0000813138,0.007214473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01087287,"threshold_uncertainty_score":0.02184159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1980574114421506,"score_gpt":0.4002445677797673,"score_spread":0.2021871563376167,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}